Abstract: Water pipeline leakage is a critical challenge that leads to the wastage of valuable water resources, increased operational costs, infrastructure damage, and environmental hazards. Traditional methods of leak detection, such as manual inspections and acoustic sensing, often suffer from high labor costs, time inefficiency, and limited detection accuracy. Therefore, there is a growing demand for automated, intelligent, and real-time leak detection systems that can accurately identify leaks and prevent potential water losses. In this project, we propose an AI-powered pipeline leakage detection system by developing a custom one-dimensional Time-Series Dense Net model integrated with multi-sensor data fusion. The system employs an array of Light Dependent Resistor (LDR) sensors to monitor changes in light intensity within the pipeline, temperature sensors to detect unusual heat variations, and wet sensors to identify the presence of leaked water. These sensors are strategically placed along the pipeline network to ensure comprehensive monitoring of potential leakage points. An ESP32 microcontroller is utilized to collect real-time sensor data, preprocess the readings, and transmit them to a central processing unit for analysis. the collected time-series data is fed into a customized Time-Series Dense Net model, which is optimized to process sequential data efficiently. Dense Net’s architecture, known for feature reuse and gradient flow efficiency, is adapted to handle one-dimensional sensor input, enabling it to detect subtle, complex patterns associated with water leaks. By leveraging the strengths of Dense Net, the proposed model ensures high accuracy in distinguishing normal pi line conditions from potential leaks based on real-time sensor fluctuations. Additionally, the system is designed to provide early warning notifications through an IoT-enabled dashboard that visualizes sensor readings, predicts leakage probability, and alerts maintenance personnel via SMS, email, or mobile app notifications. This proactive approach minimizes water losses, reduces operational costs, and enhances pipeline maintenance efficiency.

| DOI: 10.17148/IJIREEICE.2025.13342